lhn384's Stars
arctanbilibili/PMSM-parameter-online-identifcation
Based on C89, 3 methods
ArizaWilmerUTAS/Multi-Output-GPs-Identification-SHIP
System indetification of Ship Dynamic System with Multi Output Gaussian Processes
Dynamic-Systems-and-GP/GPdyn
Gaussian Process Model Dynamic System Identification Toolbox for Matlab
lucasrm25/Gaussian-Process-based-Model-Predictive-Control
Project for the course "Statistical Learning and Stochastic Control" at University of Stuttgart
IBRAAHIIMM/MLP-for-Curve-Fitting-and-Classification
simedcn/Curve-Fitting-Using-Genetic-Algorithm
Amirkia1998/Curve-Fitting-GA
🧬 Genetic Algorithm Curve Fitting in MATLAB: Optimize curve parameters to find the best fit for your data points using Genetic Algorithms. Explore various fitness functions for accurate curve modeling.
Ilia-Abolhasani/polynomial-curve-fitting
Polynomial Curve Fitting using PSO algorithm in MATLAB for accurate data modeling, prediction, and educational purposes.
avionicscode/Robust-Attitude-Controller-for-UAV-Using-Dynamic-Inversion-and-Extended-State-Observer-controller
A robust feedback linearization controller is presented for attitude control of an unmanned aerial vehicle (UAV). The objective of this controller is to make the roll angle, pitch angle, and yaw angle track the given trajectories(commands) respectively. This design is developed using dynamic inversion and extended state observer (ESO). Firstly, dynamic inversion is used to linearize and decouple UAV attitude system into three single-input-single-output (SISO) systems, then three proportional-derivative (PD) controllers are designed for these linearized systems. Extended state observers are used to estimate and compensate unmodeled dynamics and extent disturbances. Simulation results show that the proposed controller is effective and robust.
attaoveisi/Extended_State_Observer
a disturbance rejection-based solution to the problem of robust output regulation of linear systems. The difference between the underlying plant and its nominal mathematical model is represented by two classes of disturbances. The first class is generated by an autonomous linear system while the other class has no specific dynamical structure. Robustness against the first disturbance class is achieved by the internal model principle. Next, in the framework of disturbance rejection control, an extended state observer (ESO) is designed to estimate and compensate for the second class of disturbances. As a result, the proposed output regulation method can deal with a vast range of uncertainties. The stability of the closed loop system is investigated and results on practical regulation are drawn. Keywords—Output regulation, extended state observer, disturbance rejection, linear system, robustness
attaoveisi/Disturbance_observer
In this note, disturbance rejection control (DRC) based on unknown input observation (UIO), and disturbance-observer based control (DOBC) methods are revisited for a class of MIMO systems with mismatch disturbance conditions. In both of these methods, the estimated disturbance is considered to be in the feedback channel. The disturbance term could represent either unknown mismatched signals penetrating the states, or unknown dynamics not captured in the modeling process, or physical parameter variations not accounted for in the mathematical model of the plant. Unlike the high-gain approaches and variable structure methods, a systematic synthesis of the state/disturbance observer-based controller is carried out. For this purpose, first, using a series of singular value decompositions, the linearized plant is transformed into disturbance-free and disturbance-dependent subsystems. Then, functional state reconstruction based on generalized detectability concept is proposed for the disturbance-free part. Then, a DRC based on quadratic stability theorem is employed to guarantee the performance of the closed-loop system. An important contribution offered in this article is the independence of the estimated disturbance from the control input which seem to be missing in the literature for disturbance decoupling problems. In the second method, DOBC is reconsidered with the aim of achieving a high level of robustness against modeling uncertainties and matched/mismatched disturbances, while at the same time retaining performance. Accordingly, unlike the first method, DRC, full information state observation is developed independent of the disturbance estimation. An advantage of such a combination is that disturbance estimation does not involve output derivatives. Finally, the case of systems with matched disturbances is presented as a corollary of the main results.
arbabiha/KoopmanMPC_for_flowcontrol
A data-driven framework for control of nonlinear flows with Koopman Model Predictive Control
aalanwar/Data-Driven-Predictive-Control
Data-Driven Predictive Control
vipulkumbhar/AuE893_Robust_Predictive_Control
This repository contains classwork and practice examples based on Model Predictive Control. Robust and Stochastic control methods applied to and studied for linear/non-linear plants.
Eric-Bradford/UKF-SNMPC
This repository contains the source code for “Unscented Kalman filter stochastic nonlinear model predictive control” (UKF-SNMPC).
brunofloriano/NN_MPC
Neural network-based (NN) Model Predictive Control (MPC) algorithm to control a multi-agent system (MAS) with stochastic communication topology.
SaidGuerazem/MPC-RNN-Controller
This repository contains code for implementing a Recurrent Neural Network (RNN) Model Predictive Controller (MPC) for a 4-tank coupled system, as part of My Master's degree thesis project.
zhang-zengjie/dl-vehicle-mpc
vsd401/Learning-based-MPC
amoat7/robotic-manipulator-nn-hysteresis
Code for paper: Adaptive Neural Network Control of a Robotic Manipulator With Backlash hysteresis. http://ieeexplore.ieee.org/document/7866058/
EmAdd9/ANN-PID-Controller
Design of Optimal PID Controller for the Speed Control of DC Motor by Using Artificial Neural Network.
kietpa/DC-Motor_EKF_PI-PSO
DC Motor Speed Control with EKF & PI-PSO. University final project.
saeedsheikhmemari/DC-motor-control-by-recurrent-neural-networks.
gyani-codes/FOPID-tuner-using-GA
A fractional order PID controller is tuned using genetic algorithm , to control the speed of DC motor
frank1ma/DDRTC-of-UMSs
This paper presents a data-driven control design framework to achieve robust tracking control without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages the differential flatness property of linearized systems and online estimation and compensation of disturbances by active disturbance rejection control (ADRC). The differentially flat output is derived directly from measured data with unknown dynamics and parameters of UMS by the flat output identification (FOID) algorithm. A reduced nominal model of UMS is proposed to simplify the process of finding flat output and trajectory planning. Technique of sparse regression is applied to identify the relationships between flat output and system states, which reduces the order of the well-known extended state observer (ESO) and thereby make the ESO more effective for both trajectory planning and tracking in terms …
pharasha/Unscented-Kalman-Filter
An implementation of an Unscented Kalman Filter (UKF) for parameter identification on MATLAB.
MarekWadinger/RLS_identification
An algorithm for RLS model parameters identification using MATLAB.
joelpaulson/ADCHEM_ML_MPC_Workshop_2021
Code associated with the ADCHEM 2021 Workshop on Machine Learning and Model Predictive Control
joelpaulson/DYCOPS22_ML_MPC_Workshop
Code associated with the DYCOPS 2022 Workshop on Exploiting Machine Learning Methods for Approximation and Design of Model Predictive Control
dingshansun/RL-based-PMPC
Relevant codes of the paper ``Adaptive Parameterized Model Predictive Control Based on Reinforcement Learning: A Synthesis Framework"